In general, the performance of automatic speech recognition (ASR) systems degrades when the ASR systems are deployed in real services environments. The degradation of ASR system performance is typically caused by conditions such as background noise, spontaneous speech, and communication noise. A majority of existing ASR systems employ noise-robust algorithms designed to mitigate the effects of noise on the input speech. Unfortunately, the majority of existing algorithms are specifically designed to reduce one particular type of noise at the expense of being more susceptible to other types of noise. Furthermore, the majority of existing algorithms were reverse-engineered using artificial noise environments defined by the algorithm designers, as opposed to the using real services environments to design automatic speech recognition algorithms. As such, existing speech interpretation word accuracy prediction algorithms, which often use measures such as confidence score, are ineffective and often inaccurate.
Accordingly, a need exists in the art for an improved method and apparatus for predicting a word accuracy associated with an interpretation of speech data generated by an automatic speech recognition system.